Analyzing Large Scale Qualitative Data with Viable and GPT-4

Viable uses fine-tuned GPT-4 models to perform in-depth analysis of large-scale qualitative data, allowing businesses to extract actionable insights from unstructured feedback without the need for manual review. This approach enables companies to improve Net Promoter Scores (NPS), reduce support ticket volumes, and inform product roadmaps more efficiently than traditional summarization tools.

Analysis vs. Summarization in Qualitative Data

Qualitative data analysis requires a different technical approach than simple summarization. While summarization focuses on compressing information, analysis adds necessary context to ensure true comprehension of customer sentiment.

Unstructured text, such as support tickets and online reviews, often contains ambiguity, sarcasm, and negation. Simple summarization tools can overlook these nuances, potentially distorting data and leading to flawed business decisions. Viable addresses this by fine-tuning OpenAI's Large Language Models (LLMs), specifically GPT-4, to handle the scale and complexity of qualitative data analysis with higher accuracy.

Scaling Insights from Unstructured Data

Viable's platform integrates with data sources such as Zendesk, Intercom, and Gong to provide automated analysis through continuous syncing. The platform converts unstructured feedback into structured insights using the following capabilities:

  • Thematic Categorization: Data is automatically categorized into themes in a few clicks.
  • Week-over-Week Analysis: The platform provides temporal analysis to help businesses understand churn risk and the context behind their data.
  • User Profiling: The system identifies the user profiles of those providing specific feedback.
  • Complex Querying: Customers can ask the AI complex questions about their specific data sets to receive targeted insights.

Business Impact and Operational Efficiency

Implementing AI-driven qualitative analysis has significantly reduced the manual labor associated with reviewing and tagging feedback. According to the platform's users, the results include:

  • Resource Savings: Some customers have saved nearly 1,000 hours per year by automating the analysis of qualitative feedback.
  • Operational Improvements: Businesses have reported reduced support ticket volumes and decreased customer churn.

As Kalie Bishop, VP of Customer Support at Sticker Mule, stated:

"We’ve revolutionized our approach, using Viable’s powerful insights to swiftly identify areas of improvement and save our managers hundreds of hours."

Dan Erickson, CEO of Viable, emphasizes that the use of GPT-4's advanced NLP capabilities allows the platform to deliver nuanced insights in a fraction of the time required for human analysis, enabling data-driven decisions based on the entirety of a company's data rather than just quantitative KPIs.

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